@inbook{ee4320b9cbb044e59182e3ebef3b1513,
title = "Deep Boltzmann machines and the centering trick",
abstract = "Deep Boltzmann machines are in theory capable of learning efficient representations of seemingly complex data. Designing an algorithm that effectively learns the data representation can be subject to multiple difficulties. In this chapter, we present the {"}centering trick{"} that consists of rewriting the energy of the system as a function of centered states. The centering trick improves the conditioning of the underlying optimization problem and makes learning more stable, leading to models with better generative and discriminative properties.",
keywords = "Deep Boltzmann machine, centering, optimization, reparameterization, representations, unsupervised learning",
author = "Gr{\'e}goire Montavon and M{\"u}ller, {Klaus Robert}",
note = "Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",
year = "2012",
doi = "10.1007/978-3-642-35289-8_33",
language = "English",
isbn = "9783642352881",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "621--637",
booktitle = "Neural Networks",
}